System

Abstract

An object of a system according to an embodiment is to provide a specific suggestion or guidance for improving the quality of a presentation.SOLUTION: A system includes an analysis part, a proposal part, and an instruction part. The analysis unit analyzes the presentation content of the user using the generated AI. The proposal unit makes a specific proposal based on the presentation content analyzed by the analysis unit. The guidance unit provides guidance to the user on the basis of the suggestion provided by the suggestion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to efficiently provide specific suggestions and guidance to improve the quality of presentations.

[0005] The system according to the embodiment aims to provide specific suggestions and guidance for improving the quality of presentations. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and an instruction unit. The analysis unit analyzes the content of a user's presentation using a generation AI. The proposal unit makes specific proposals based on the presentation content analyzed by the analysis unit. The instruction unit provides instruction to the user based on the proposals provided by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific suggestions and guidance to improve the quality of the presentation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A coaching system according to an embodiment of the present invention is a system that uses generative AI to enhance a user's presentation skills, thereby improving the user's presentation skills and enabling them to deliver a presentation that captivates an audience.

[0029] A coaching system according to an embodiment includes an analysis unit, a suggestion unit, and an instruction unit. The analysis unit analyzes the content of a user's presentation. For example, the analysis unit uses a generation AI to perform text analysis and analyze the structure and content of the presentation. The analysis unit can also perform audio analysis to analyze the user's vocalization and intonation. The analysis unit can also perform emotion analysis to analyze changes in the user's emotions. The suggestion unit makes specific suggestions based on the presentation content analyzed by the analysis unit. For example, the suggestion unit uses a generation AI to suggest improvements to the introduction and conclusion of the presentation. The suggestion unit can also suggest storytelling methods and how to use visual materials. The suggestion unit can also suggest improvements to the user's vocalization and intonation. The instruction unit provides instruction to the user based on the suggestions provided by the suggestion unit. For example, the instruction unit uses a generation AI to provide specific feedback to the user. The instruction unit can also support the user's presentation practice and provide feedback in real time. The instruction unit can also provide specific advice to improve the user's presentation skills. As a result, the coaching system according to the embodiment can provide specific suggestions and guidance to improve the presentation skills of the user.

[0030] The analysis unit analyzes the user's past presentation data and can track and suggest improvements over time. For example, the analysis unit uses generative AI to collect the user's past presentation data and analyze the evaluations and feedback for each presentation. This allows the analysis unit to identify past areas for improvement and make specific suggestions for the next presentation. The analysis unit also analyzes the user's past presentation data in chronological order to extract trends and patterns for improvement. For example, if the introduction needs improvement, the analysis unit can suggest specific ways to do so. The analysis unit also analyzes the user's past presentation data and tracks improvements to specific slides or sections over time. This allows the user to continuously improve the quality of their presentations. This allows the analysis unit to track improvements to the user's presentations over time and make specific suggestions for the next presentation.

[0031] The analysis unit analyzes the content of the user's presentation and compares it with success stories of competitors to make specific improvement suggestions. The analysis unit, for example, uses generation AI to analyze the content of the user's presentation and compares it with success stories of competitors. For example, it refers to the presentation style and content of competitors and makes specific improvement suggestions. The analysis unit also analyzes the content of the user's presentation and compares it with success stories of competitors to make suggestions for effective introductions and conclusions. This allows the user to create a competitive presentation. The analysis unit also uses generation AI to analyze the content of the user's presentation and compares it with success stories of competitors to make suggestions for how to use visual materials and storytelling methods. This allows the user to give a more attractive presentation. This allows the content of the user's presentation to be compared with success stories of competitors and makes specific improvement suggestions.

[0032] The analysis unit can analyze presentation content in different languages ​​and make multilingual suggestions. The analysis unit, for example, uses generation AI to automatically translate the user's presentation content into different languages ​​and analyze the translation results. This makes specific multilingual suggestions. The analysis unit also analyzes the user's presentation content and suggests effective expressions and storytelling methods in different languages. For example, it makes suggestions that take cultural differences into consideration. The analysis unit also uses generation AI to analyze presentation content in different languages ​​and suggests multilingual visual materials and slides. This allows the user to give effective international presentations. This makes it possible to analyze presentation content in different languages ​​and make multilingual suggestions.

[0033] The analysis unit can analyze the content of a user's presentation and make suggestions from the perspective of different industries. For example, the analysis unit can use generative AI to analyze the content of a user's presentation and make suggestions based on success stories and best practices from different industries. This allows the user to find areas for improvement from a new perspective. The analysis unit can also analyze the content of a user's presentation and make specific suggestions for improvement from the perspective of different industries. For example, incorporating a marketing perspective into a presentation in the technical field. The analysis unit can also analyze the content of a user's presentation using generative AI and suggest visual materials and storytelling methods from the perspective of different industries. This allows the user to make presentations with a multifaceted approach. This allows the analysis of the content of a user's presentation from the perspective of different industries and make specific suggestions.

[0034] The guidance unit can analyze the user's language and make suggestions that incorporate literary expressions and poetic elements. For example, the guidance unit can use a generative AI to analyze the content of the user's presentation and suggest language that incorporates literary expressions and poetic elements. For example, it can suggest expressions that use metaphors and symbols. The guidance unit can also analyze the user's language and suggest specific phrases and sentences that incorporate literary elements. This makes the presentation more appealing. The guidance unit can also use a generative AI to analyze the content of the user's presentation and suggest storytelling methods that incorporate poetic elements. For example, it can suggest expressions that take rhythm and rhyme into consideration. This allows the guidance unit to analyze the user's language and make suggestions that incorporate literary expressions and poetic elements.

[0035] The guidance unit can analyze the user's storytelling and make suggestions that incorporate film and theater techniques. The guidance unit can, for example, use generative AI to analyze the user's storytelling and make specific suggestions that incorporate film and theater techniques. For example, it can suggest scene transitions and character portrayals. The guidance unit can also analyze the user's storytelling and suggest effective introductions and conclusions that incorporate film and theater techniques. This makes the presentation more dramatic. The guidance unit can also analyze the user's storytelling using generative AI and suggest visual materials and slides that incorporate film and theater techniques. This allows the user to give a visually appealing presentation. The guidance unit can also analyze the user's storytelling and make suggestions that incorporate film and theater techniques.

[0036] The coaching unit can analyze the content of the user's presentation and suggest visual storytelling. The coaching unit can, for example, use generative AI to analyze the content of the user's presentation and make specific suggestions for visual storytelling. For example, it can suggest effective slide designs and ways to use videos. The coaching unit can also analyze the content of the user's presentation and suggest specific visual materials and graphics to enhance the visual storytelling. This makes the presentation more visually appealing. The coaching unit can also analyze the content of the user's presentation and suggest ways to visually storytelling. For example, it can suggest slide layouts, color usage, and video insertion points. This makes it possible to analyze the content of the user's presentation and suggest visual storytelling.

[0037] The coaching unit can analyze the content of a user's presentation and suggest storytelling that incorporates music and sound effects. The coaching unit can, for example, use a generative AI to analyze the content of a user's presentation and suggest specific storytelling that incorporates music and sound effects. For example, it can suggest ways to use music and sound effects to enhance emotions. The coaching unit can also analyze the content of a user's presentation and suggest effective introductions and conclusions that incorporate music and sound effects. This makes the presentation more moving. The coaching unit can also analyze the content of a user's presentation using a generative AI and suggest visual materials and slides that incorporate music and sound effects. This allows the user to give a presentation that engages the audience both visually and aurally. The coaching unit can analyze the content of a user's presentation and suggest storytelling that incorporates music and sound effects.

[0038] The instructor can analyze the user's body language and make specific suggestions for improving their movements and posture. The instructor can, for example, use a generative AI to analyze the user's body language during a presentation and make specific suggestions for improving their movements and posture. For example, it can suggest hand movements and how to use their gaze. The instructor can also analyze the user's body language and suggest effective posture and movements. This allows the user to give a presentation with confidence. The instructor can also use a generative AI to analyze the user's body language during a presentation, point out specific areas for improvement, and suggest practice methods. This allows the user to use body language effectively. The instructor can analyze the user's body language and make specific suggestions for improving their movements and posture.

[0039] The coaching department can analyze the tone and rhythm of the user's voice and suggest effective ways to use their voice. For example, the coaching department can use generative AI to analyze the tone and rhythm of the user's voice during a presentation and suggest effective ways to use their voice. For example, the coaching department can suggest how to raise the voice and how to keep the rhythm when emphasis should be placed on certain points. The coaching department can also analyze the tone and rhythm of the user's voice, point out specific areas for improvement, and suggest practice methods. This allows the user to give a presentation with confidence. The coaching department can also use generative AI to analyze the tone and rhythm of the user's voice during a presentation and suggest effective ways to use their voice. This allows the user to give a presentation that engages the audience. This allows the coaching department to analyze the tone and rhythm of the user's voice and suggest effective ways to use their voice.

[0040] The coaching unit can analyze the content of the user's speech and suggest effective speech techniques in different cultures. The coaching unit can, for example, use a generative AI to analyze the content of the user's speech and suggest effective speech techniques in different cultures. For example, it can suggest specific expressions and body language that take cultural differences into consideration. The coaching unit can also analyze the content of the user's speech and suggest effective introductions and conclusions in different cultures. This allows the user to give effective international presentations. The coaching unit can also analyze the content of the user's speech using a generative AI and suggest effective visual materials and slides in different cultures. This allows the user to give multicultural presentations. This allows the coaching unit to analyze the content of the user's speech and suggest effective speech techniques in different cultures.

[0041] The instructor can analyze the content of the user's speech and suggest speech practice using virtual reality (VR). The instructor can, for example, use a generative AI to analyze the content of the user's speech and suggest a specific speech practice method using virtual reality (VR). For example, it can provide a practice scenario in front of a virtual audience. The instructor can also analyze the content of the user's speech and suggest an effective practice method using VR. This allows the user to practice in an environment that is close to an actual presentation. The instructor can also analyze the content of the user's speech using a generative AI and suggest a practice method using visual materials and slides using VR. This allows the user to give a visually appealing presentation. This allows the instructor to analyze the content of the user's speech and suggest speech practice using virtual reality (VR).

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The coaching system can further analyze the content of the user's presentation and suggest effective speech techniques for different cultures. For example, it can suggest specific expressions and body language that take cultural differences into account. The analysis unit also analyzes the content of the user's speech and suggests effective introductions and conclusions for different cultures. This allows the user to give effective international presentations. The analysis unit also uses generative AI to analyze the content of the user's speech and suggests effective visual materials and slides for different cultures. This allows the user to give multicultural presentations.

[0044] The coaching system can further analyze the content of the user's presentation and suggest speech practice using virtual reality (VR). For example, it can provide a practice scenario in front of a virtual audience. The analysis unit also analyzes the content of the user's speech and suggests effective practice methods using VR. This allows the user to practice in an environment that is close to an actual presentation. The analysis unit also analyzes the content of the user's speech using generative AI and suggests practice methods using visual materials and slides using VR. This allows the user to give a visually appealing presentation.

[0045] The coaching system can further analyze the content of the user's presentation and suggest storytelling that incorporates music and sound effects. For example, it can suggest ways to use music and sound effects to enhance emotions. The analysis unit also analyzes the content of the user's presentation and suggests effective introductions and conclusions that incorporate music and sound effects. This makes the presentation more moving. The analysis unit also uses generative AI to analyze the content of the user's presentation and suggests visual materials and slides that incorporate music and sound effects. This allows the user to give a presentation that engages the audience both visually and aurally.

[0046] The coaching system can further analyze the content of the user's presentation and make suggestions that incorporate literary expressions and poetic elements. For example, it can suggest expressions that use metaphors and symbols. The analysis unit also analyzes the user's language usage and suggests specific phrases and sentences that incorporate literary elements, making the presentation more appealing. The analysis unit also uses generative AI to analyze the content of the user's presentation and suggests storytelling methods that incorporate poetic elements. For example, it can suggest expressions that take rhythm and rhyme into consideration.

[0047] The coaching system can further analyze the content of the user's presentation and make suggestions incorporating film and theater techniques. For example, it can suggest scene transitions and character portrayals. The analysis unit also analyzes the user's storytelling and suggests effective introductions and conclusions that incorporate film and theater techniques, making the presentation more dramatic. The analysis unit also uses generative AI to analyze the user's storytelling and suggests visual materials and slides that incorporate film and theater techniques, allowing the user to deliver visually appealing presentations.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The analysis unit analyzes the content of the user's presentation. For example, it uses generative AI to perform text analysis and analyze the structure and content of the presentation. It can also perform audio analysis to analyze the user's vocalizations and intonation. It can also perform emotion analysis to analyze changes in the user's emotions. Step 2: The proposal section makes specific suggestions based on the presentation content analyzed by the analysis section. For example, the proposal section uses generative AI to suggest improvements to the introduction and conclusion of the presentation. It can also suggest improvements to storytelling methods, the use of visual materials, and the user's pronunciation and intonation. Step 3: The coaching unit coaches the user based on the suggestions provided by the suggestion unit. For example, the coaching unit provides specific feedback to the user using a generative AI. The coaching unit can also support the user in practicing their presentation and provide feedback in real time. Furthermore, the coaching unit can provide specific advice to improve the user's presentation skills.

[0050] (Example 2) A coaching system according to an embodiment of the present invention is a system that uses generative AI to enhance a user's presentation skills, thereby improving the user's presentation skills and enabling them to deliver a presentation that captivates an audience.

[0051] A coaching system according to an embodiment includes an analysis unit, a suggestion unit, and an instruction unit. The analysis unit analyzes the content of a user's presentation. For example, the analysis unit uses a generation AI to perform text analysis and analyze the structure and content of the presentation. The analysis unit can also perform audio analysis to analyze the user's vocalization and intonation. The analysis unit can also perform emotion analysis to analyze changes in the user's emotions. The suggestion unit makes specific suggestions based on the presentation content analyzed by the analysis unit. For example, the suggestion unit uses a generation AI to suggest improvements to the introduction and conclusion of the presentation. The suggestion unit can also suggest storytelling methods and how to use visual materials. The suggestion unit can also suggest improvements to the user's vocalization and intonation. The instruction unit provides instruction to the user based on the suggestions provided by the suggestion unit. For example, the instruction unit uses a generation AI to provide specific feedback to the user. The instruction unit can also support the user's presentation practice and provide feedback in real time. The instruction unit can also provide specific advice to improve the user's presentation skills. As a result, the coaching system according to the embodiment can provide specific suggestions and guidance to improve the presentation skills of the user.

[0052] The analysis unit analyzes the user's past presentation data and can track and suggest improvements over time. For example, the analysis unit uses generative AI to collect the user's past presentation data and analyze the evaluations and feedback for each presentation. This allows the analysis unit to identify past areas for improvement and make specific suggestions for the next presentation. The analysis unit also analyzes the user's past presentation data in chronological order to extract trends and patterns for improvement. For example, if the introduction needs improvement, the analysis unit can suggest specific ways to do so. The analysis unit also analyzes the user's past presentation data and tracks improvements to specific slides or sections over time. This allows the user to continuously improve the quality of their presentations. This allows the analysis unit to track improvements to the user's presentations over time and make specific suggestions for the next presentation.

[0053] The analysis unit analyzes the content of the user's presentation and compares it with success stories of competitors to make specific improvement suggestions. The analysis unit, for example, uses generation AI to analyze the content of the user's presentation and compares it with success stories of competitors. For example, it refers to the presentation style and content of competitors and makes specific improvement suggestions. The analysis unit also analyzes the content of the user's presentation and compares it with success stories of competitors to make suggestions for effective introductions and conclusions. This allows the user to create a competitive presentation. The analysis unit also uses generation AI to analyze the content of the user's presentation and compares it with success stories of competitors to make suggestions for how to use visual materials and storytelling methods. This allows the user to give a more attractive presentation. This allows the content of the user's presentation to be compared with success stories of competitors and makes specific improvement suggestions.

[0054] The analysis unit uses the emotion estimation function to analyze changes in the user's emotions during a presentation and can make suggestions that match the peak of their emotions. The analysis unit, for example, uses the emotion estimation function to analyze changes in the user's emotions during a presentation in real time. For example, it suggests points to emphasize or visual materials to use when emotions are at their peak. The analysis unit also analyzes changes in the user's emotions during a presentation and suggests storytelling methods that match the peak of their emotions. This allows for a presentation that engages the audience's emotions. The analysis unit also uses the emotion estimation function to analyze changes in the user's emotions during a presentation and suggests how to use body language and tone of voice that match the peak of their emotions. This allows the user to give a presentation with confidence. This allows for analysis of changes in the user's emotions during a presentation and can make suggestions that match the peak of their emotions.

[0055] The analysis unit can analyze presentation content in different languages ​​and make multilingual suggestions. The analysis unit, for example, uses generation AI to automatically translate the user's presentation content into different languages ​​and analyze the translation results. This makes specific multilingual suggestions. The analysis unit also analyzes the user's presentation content and suggests effective expressions and storytelling methods in different languages. For example, it makes suggestions that take cultural differences into consideration. The analysis unit also uses generation AI to analyze presentation content in different languages ​​and suggests multilingual visual materials and slides. This allows the user to give effective international presentations. This makes it possible to analyze presentation content in different languages ​​and make multilingual suggestions.

[0056] The analysis unit can analyze the content of a user's presentation and make suggestions from the perspective of different industries. For example, the analysis unit can use generative AI to analyze the content of a user's presentation and make suggestions based on success stories and best practices from different industries. This allows the user to find areas for improvement from a new perspective. The analysis unit can also analyze the content of a user's presentation and make specific suggestions for improvement from the perspective of different industries. For example, incorporating a marketing perspective into a presentation in the technical field. The analysis unit can also analyze the content of a user's presentation using generative AI and suggest visual materials and storytelling methods from the perspective of different industries. This allows the user to make presentations with a multifaceted approach. This allows the analysis of the content of a user's presentation from the perspective of different industries and make specific suggestions.

[0057] The analysis unit uses the emotion estimation function to analyze the audience's emotional reactions to the content of the user's presentation in real time and can provide instant feedback. The analysis unit, for example, uses the emotion estimation function to analyze the audience's emotional reactions to the content of the user's presentation in real time. For example, it analyzes the audience's facial expressions and voices and calculates an emotion score. The analysis unit also builds a system that provides instant feedback to the user based on the audience's emotional reaction data. For example, it presents areas for improvement and points that need to be strengthened during the presentation in real time. The analysis unit also uses the emotion estimation function to analyze the audience's emotional reactions to the content of the user's presentation and makes specific suggestions that match the emotional peaks. This allows the user to give a presentation that engages the audience. This allows the audience's emotional reactions to the content of the user's presentation to be analyzed in real time and can provide instant feedback.

[0058] The guidance unit can analyze the user's language and make suggestions that incorporate literary expressions and poetic elements. For example, the guidance unit can use a generative AI to analyze the content of the user's presentation and suggest language that incorporates literary expressions and poetic elements. For example, it can suggest expressions that use metaphors and symbols. The guidance unit can also analyze the user's language and suggest specific phrases and sentences that incorporate literary elements. This makes the presentation more appealing. The guidance unit can also use a generative AI to analyze the content of the user's presentation and suggest storytelling methods that incorporate poetic elements. For example, it can suggest expressions that take rhythm and rhyme into consideration. This allows the guidance unit to analyze the user's language and make suggestions that incorporate literary expressions and poetic elements.

[0059] The guidance unit can analyze the user's storytelling and make suggestions that incorporate film and theater techniques. The guidance unit can, for example, use generative AI to analyze the user's storytelling and make specific suggestions that incorporate film and theater techniques. For example, it can suggest scene transitions and character portrayals. The guidance unit can also analyze the user's storytelling and suggest effective introductions and conclusions that incorporate film and theater techniques. This makes the presentation more dramatic. The guidance unit can also analyze the user's storytelling using generative AI and suggest visual materials and slides that incorporate film and theater techniques. This allows the user to give a visually appealing presentation. The guidance unit can also analyze the user's storytelling and make suggestions that incorporate film and theater techniques.

[0060] The coaching unit can analyze the content of the user's presentation and suggest visual storytelling. The coaching unit can, for example, use generative AI to analyze the content of the user's presentation and make specific suggestions for visual storytelling. For example, it can suggest effective slide designs and ways to use videos. The coaching unit can also analyze the content of the user's presentation and suggest specific visual materials and graphics to enhance the visual storytelling. This makes the presentation more visually appealing. The coaching unit can also analyze the content of the user's presentation and suggest ways to visually storytelling. For example, it can suggest slide layouts, color usage, and video insertion points. This makes it possible to analyze the content of the user's presentation and suggest visual storytelling.

[0061] The coaching unit can analyze the content of a user's presentation and suggest storytelling that incorporates music and sound effects. The coaching unit can, for example, use a generative AI to analyze the content of a user's presentation and suggest specific storytelling that incorporates music and sound effects. For example, it can suggest ways to use music and sound effects to enhance emotions. The coaching unit can also analyze the content of a user's presentation and suggest effective introductions and conclusions that incorporate music and sound effects. This makes the presentation more moving. The coaching unit can also analyze the content of a user's presentation using a generative AI and suggest visual materials and slides that incorporate music and sound effects. This allows the user to give a presentation that engages the audience both visually and aurally. The coaching unit can analyze the content of a user's presentation and suggest storytelling that incorporates music and sound effects.

[0062] The instruction unit can use the emotion estimation function to analyze the audience's emotional response to the user's storytelling and suggest expressions that are likely to resonate emotionally. The instruction unit, for example, uses the emotion estimation function to analyze the audience's emotional response to the user's storytelling in real time. For example, the instruction unit can suggest expressions and phrases that are likely to resonate emotionally based on an emotion score. The instruction unit can also suggest specific expressions and storytelling methods that are likely to resonate emotionally with the user based on the audience's emotional response data. This allows the user to give a presentation that engages the audience's emotions. The instruction unit can also use the emotion estimation function to analyze the audience's emotional response to the user's storytelling and suggest visual materials and slides that match the emotional peaks. This allows the user to give a presentation that is likely to resonate emotionally. This allows the instruction unit to analyze the audience's emotional response to the user's storytelling and suggest expressions that are likely to resonate emotionally.

[0063] The instructor can analyze the user's body language and make specific suggestions for improving their movements and posture. The instructor can, for example, use a generative AI to analyze the user's body language during a presentation and make specific suggestions for improving their movements and posture. For example, it can suggest hand movements and how to use their gaze. The instructor can also analyze the user's body language and suggest effective posture and movements. This allows the user to give a presentation with confidence. The instructor can also use a generative AI to analyze the user's body language during a presentation, point out specific areas for improvement, and suggest practice methods. This allows the user to use body language effectively. The instructor can analyze the user's body language and make specific suggestions for improving their movements and posture.

[0064] The coaching department can analyze the tone and rhythm of the user's voice and suggest effective ways to use their voice. For example, the coaching department can use generative AI to analyze the tone and rhythm of the user's voice during a presentation and suggest effective ways to use their voice. For example, the coaching department can suggest how to raise the voice and how to keep the rhythm when emphasis should be placed on certain points. The coaching department can also analyze the tone and rhythm of the user's voice, point out specific areas for improvement, and suggest practice methods. This allows the user to give a presentation with confidence. The coaching department can also use generative AI to analyze the tone and rhythm of the user's voice during a presentation and suggest effective ways to use their voice. This allows the user to give a presentation that engages the audience. This allows the coaching department to analyze the tone and rhythm of the user's voice and suggest effective ways to use their voice.

[0065] The coaching unit can use the emotion estimation function to analyze the user's level of confidence during speech and provide specific advice to increase confidence. The coaching unit, for example, uses the emotion estimation function to analyze the user's level of confidence during speech in real time. For example, the coaching unit can provide specific advice to increase confidence based on the emotion score. The coaching unit can also analyze the user's level of confidence during speech, point out specific areas for improvement, and suggest practice methods to increase confidence. This allows the user to give a presentation with confidence. The coaching unit can also use the emotion estimation function to analyze the user's level of confidence during speech and suggest specific body language and voice usage to increase confidence. This allows the user to give a presentation with confidence. This allows the user to give a presentation with confidence. The coaching unit can analyze the user's level of confidence during speech and provide specific advice to increase confidence.

[0066] The coaching unit can analyze the content of the user's speech and suggest effective speech techniques in different cultures. The coaching unit can, for example, use a generative AI to analyze the content of the user's speech and suggest effective speech techniques in different cultures. For example, it can suggest specific expressions and body language that take cultural differences into consideration. The coaching unit can also analyze the content of the user's speech and suggest effective introductions and conclusions in different cultures. This allows the user to give effective international presentations. The coaching unit can also analyze the content of the user's speech using a generative AI and suggest effective visual materials and slides in different cultures. This allows the user to give multicultural presentations. This allows the coaching unit to analyze the content of the user's speech and suggest effective speech techniques in different cultures.

[0067] The instructor can analyze the content of the user's speech and suggest speech practice using virtual reality (VR). The instructor can, for example, use a generative AI to analyze the content of the user's speech and suggest a specific speech practice method using virtual reality (VR). For example, it can provide a practice scenario in front of a virtual audience. The instructor can also analyze the content of the user's speech and suggest an effective practice method using VR. This allows the user to practice in an environment that is close to an actual presentation. The instructor can also analyze the content of the user's speech using a generative AI and suggest a practice method using visual materials and slides using VR. This allows the user to give a visually appealing presentation. This allows the instructor to analyze the content of the user's speech and suggest speech practice using virtual reality (VR).

[0068] The coaching unit can use the emotion estimation function to analyze the audience's emotional reactions to the user's speech and provide feedback to boost confidence in real time. For example, the coaching unit uses the emotion estimation function to analyze the audience's emotional reactions to the user's speech in real time. For example, the coaching unit provides specific feedback to boost confidence based on the emotion score. The coaching unit also builds a system that provides feedback to boost confidence in real time to the user based on the audience's emotional reaction data. For example, the coaching unit presents areas for improvement and points to strengthen during the presentation in real time. The coaching unit also uses the emotion estimation function to analyze the audience's emotional reactions to the user's speech and suggests specific body language and voice usage to boost confidence. This allows the user to give a presentation with confidence. This allows the coaching unit to analyze the audience's emotional reactions to the user's speech and provide feedback to boost confidence in real time.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The coaching system can further analyze the content of the user's presentation and suggest effective speech techniques for different cultures. For example, it can suggest specific expressions and body language that take cultural differences into account. The analysis unit also analyzes the content of the user's speech and suggests effective introductions and conclusions for different cultures. This allows the user to give effective international presentations. The analysis unit also uses generative AI to analyze the content of the user's speech and suggests effective visual materials and slides for different cultures. This allows the user to give multicultural presentations.

[0071] The coaching system can further analyze the content of the user's presentation and suggest speech practice using virtual reality (VR). For example, it can provide a practice scenario in front of a virtual audience. The analysis unit also analyzes the content of the user's speech and suggests effective practice methods using VR. This allows the user to practice in an environment that is close to an actual presentation. The analysis unit also analyzes the content of the user's speech using generative AI and suggests practice methods using visual materials and slides using VR. This allows the user to give a visually appealing presentation.

[0072] The coaching system can further analyze the content of the user's presentation and suggest storytelling that incorporates music and sound effects. For example, it can suggest ways to use music and sound effects to enhance emotions. The analysis unit also analyzes the content of the user's presentation and suggests effective introductions and conclusions that incorporate music and sound effects. This makes the presentation more moving. The analysis unit also uses generative AI to analyze the content of the user's presentation and suggests visual materials and slides that incorporate music and sound effects. This allows the user to give a presentation that engages the audience both visually and aurally.

[0073] The coaching system can further analyze the content of the user's presentation and make suggestions that incorporate literary expressions and poetic elements. For example, it can suggest expressions that use metaphors and symbols. The analysis unit also analyzes the user's language usage and suggests specific phrases and sentences that incorporate literary elements, making the presentation more appealing. The analysis unit also uses generative AI to analyze the content of the user's presentation and suggests storytelling methods that incorporate poetic elements. For example, it can suggest expressions that take rhythm and rhyme into consideration.

[0074] The coaching system can further analyze the content of the user's presentation and make suggestions incorporating film and theater techniques. For example, it can suggest scene transitions and character portrayals. The analysis unit also analyzes the user's storytelling and suggests effective introductions and conclusions that incorporate film and theater techniques, making the presentation more dramatic. The analysis unit also uses generative AI to analyze the user's storytelling and suggests visual materials and slides that incorporate film and theater techniques, allowing the user to deliver visually appealing presentations.

[0075] The coaching system can further analyze the content of the user's presentation, use an emotion estimation function to analyze the user's level of confidence during the speech, and provide specific advice to improve confidence. For example, specific advice to improve confidence is provided based on the emotion score. The analysis unit also analyzes the user's level of confidence during the speech, points out specific areas for improvement, and suggests practice methods to improve confidence. This allows the user to give a presentation with confidence. The analysis unit also uses an emotion estimation function to analyze the user's level of confidence during the speech, and suggests specific body language and voice usage to improve confidence.

[0076] The coaching system further analyzes the content of the user's presentation and uses an emotion estimation function to analyze the audience's emotional response to the user's storytelling, suggesting expressions that are likely to resonate emotionally. For example, it suggests expressions and phrases that are likely to resonate emotionally based on the emotion score. The analysis unit also suggests specific expressions and storytelling methods that are likely to resonate emotionally with the user based on the audience's emotional response data. This allows the user to give a presentation that draws in the audience's emotions. The analysis unit also uses the emotion estimation function to analyze the audience's emotional response to the user's storytelling, suggesting visual materials and slides that match the emotional peaks.

[0077] The coaching system can further analyze the content of the user's presentation and use the emotion estimation function to analyze changes in the user's emotions during the presentation and make suggestions that match the user's emotional peaks. For example, it can suggest points to emphasize or visual materials to use when emotions are at their peak. The analysis unit also analyzes changes in the user's emotions during the presentation and suggests storytelling methods that match the user's emotional peaks. This allows the user to deliver a presentation that engages the audience's emotions. The analysis unit also uses the emotion estimation function to analyze changes in the user's emotions during the presentation and suggests how to use body language and tone of voice that match the user's emotional peaks.

[0078] The coaching system can further analyze the content of the user's presentation and use an emotion estimation function to analyze the audience's emotional reactions to the content of the user's presentation in real time and provide immediate feedback. For example, it can analyze the audience's facial expressions and voices and calculate an emotion score. The analysis unit also builds a system that provides immediate feedback to the user based on the audience's emotional reaction data. For example, it can present areas for improvement and points that need to be strengthened in real time during the presentation. The analysis unit also uses the emotion estimation function to analyze the audience's emotional reactions to the content of the user's presentation and make specific suggestions that match the emotional peaks.

[0079] The coaching system can further analyze the content of the user's presentation and use an emotion estimation function to analyze the audience's emotional reactions to the user's speech and provide feedback to boost confidence in real time. For example, specific feedback to boost confidence is provided based on the emotion score. The analysis unit also builds a system that provides feedback to boost confidence in real time to the user based on the audience's emotional reaction data. For example, it presents areas for improvement and points that need to be strengthened during the presentation in real time. The analysis unit also uses the emotion estimation function to analyze the audience's emotional reactions to the user's speech and suggests specific body language and voice usage to boost confidence.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The analysis unit analyzes the content of the user's presentation. For example, it uses generative AI to perform text analysis and analyze the structure and content of the presentation. It can also perform audio analysis to analyze the user's vocalizations and intonation. It can also perform emotion analysis to analyze changes in the user's emotions. Step 2: The proposal section makes specific suggestions based on the presentation content analyzed by the analysis section. For example, the proposal section uses generative AI to suggest improvements to the introduction and conclusion of the presentation. It can also suggest improvements to storytelling methods, the use of visual materials, and the user's pronunciation and intonation. Step 3: The coaching unit coaches the user based on the suggestions provided by the suggestion unit. For example, the coaching unit provides specific feedback to the user using a generative AI. The coaching unit can also support the user in practicing their presentation and provide feedback in real time. Furthermore, the coaching unit can provide specific advice to improve the user's presentation skills.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using generative AI, an analysis unit that analyzes the content of a user's presentation; a proposal unit that makes a specific proposal based on the presentation content analyzed by the analysis unit; a guidance unit that provides guidance to the user based on the suggestion provided by the suggestion unit. A system characterized by:

2. The analysis unit Analyze the user's past presentation data, track historical improvements, and suggest them 2. The system of claim 1.

3. The analysis unit Analyze the content of the user's presentation and make specific improvement proposals by comparing it with success stories from other companies in the same industry.

2. The system of claim 1.

4. The analysis unit Analyze changes in the user's emotions during the presentation and make suggestions that match the peak of their emotions.

2. The system of claim 1.

5. The analysis unit Analyze the presentation content in different languages ​​and propose multilingual solutions 2. The system of claim 1.

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